Show pageBacklinksCite current pageExport to PDFFold/unfold allBack to top This page is read only. You can view the source, but not change it. Ask your administrator if you think this is wrong. ====== Model ====== A model is a representation or abstraction of a real-world system or concept. ===== Computational model ===== [[Computational model]]. ===== Machine learning model ===== [[Machine learning model]]. ===== Animal model ===== see [[Animal model]]. ===== Canonical model ===== see [[Canonical model]]. ===== Head model ===== [[Head model]]. [[Glioma model]] ===== Prediction model ===== [[Prediction model]]. ===== Statistical model ===== [[Statistical model]] A model with high [[error]] due to [[bias]] can fail to capture the regularities in the [[data]], resulting in an inaccurate model underfitting the data. Increasing the complexity of the model, such as adding more [[parameter]]s in the model, can reduce this [[bias]]. However, an excessively complex model, such as having too many parameters compared to the number of [[patient]]s, can describe [[random error]] or noise instead of the meaningful relationships, referred to as overfitting of the data. This results in an increase in error due to variance and a reduced generalizability to previously unseen data. The complexity of a model should, therefore, be a tradeoff between bias and [[variance]] ((Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science . 2015;349(6245):255-260.)). model.txt Last modified: 2025/05/03 10:46by administrador